Taller 8

1 Introducción

En este taller replicaremos algunas tablas y análisis de datos del siguiente artículo: https://www.sciencedirect.com/science/article/pii/S2590136223000529

2 Parte 1

En esta parte del taller continuaremos analizando los datos que analizamos en el taller pasado sobre el experimento aleatorizado de maca

2.1 Cargar paquetes

library(tidyverse)                   # Varias funciones de tidyverse (dplyr, ggplot2, etc.)
library(rio)                         # Navaja suiza de la importación de datos
library(skimr)                       # Análisis inicial de datos
library(Hmisc)                       # Análisis inicial de datos
library(janitor)                     # Para identificar duplicados
library(gt)                          # Gramatica de tablas
library(gtsummary)                   # Wrapper de gt para inv. biomedica
library(patchwork)                   # Componer, fusionar, manipular graficos ggplot2
library(knitr)                       # Para usar algunos superpoderes de Quarto (include_graphics)
library(kableExtra)

2.2 Cargar los datos

data <- import("data_derived_pf.rds")

2.3 Explorar datos

head(data)

# A tibble: 6 × 76
  record_id    age agecat agecat2 sex   nac   ingresos_per ingresos_fam prof_cat
  <labelled> <dbl> <fct>  <fct>   <fct> <fct> <fct>        <fct>        <fct>   
1 SA0001F2      33 20-44  (30,40] Male  Peru  <NA>         <NA>         Adminis…
2 SA0002F2      54 45-59  (50,60] Fema… Peru  5581-9300    5581-9300    Nurses  
3 SA0004F2      51 45-59  (50,60] Male  Peru  >=9301       >=9301       Pyshici…
4 SA0007F2      64 60+    (60,70] Male  Peru  3721-5580    3721-5580    Pyshici…
5 SA0008F2      69 60+    (60,70] Fema… Peru  <NA>         <NA>         Pyshici…
6 SA0009F2      68 60+    (60,70] Male  Peru  5581-9300    5581-9300    Pyshici…
# ℹ 67 more variables: hosp <fct>, area_cat <fct>, modal_cat <fct>,
#   height <dbl>, weight <int>, bmi <dbl>, bmicat <fct>, ncomorb <dbl>,
#   ncomorb2 <fct>, com_hta <fct>, com_diab <fct>, com_obes <fct>,
#   com_epc <fct>, com_trasp <fct>, com_cancer <fct>, com_asma <fct>,
#   com_desnut <fct>, com_hipot <fct>, com_vih <fct>, com_otro <fct>,
#   inmunosup <fct>, infprev <fct>, inf2 <fct>, boost <fct>, boost_date <date>,
#   boost_react <fct>, boost_react_dolor <fct>, boost_react_enroj <fct>, …

glimpse(data)

Rows: 595
Columns: 76
$ record_id             <labelled> "SA0001F2", "SA0002F2", "SA0004F2", "SA0007…
$ age                   <dbl> 33, 54, 51, 64, 69, 68, 41, 61, 29, 20, 61, 59, …
$ agecat                <fct> 20-44, 45-59, 45-59, 60+, 60+, 60+, 20-44, 60+, …
$ agecat2               <fct> "(30,40]", "(50,60]", "(50,60]", "(60,70]", "(60…
$ sex                   <fct> Male, Female, Male, Male, Female, Male, Male, Ma…
$ nac                   <fct> Peru, Peru, Peru, Peru, Peru, Peru, Peru, Peru, …
$ ingresos_per          <fct> NA, 5581-9300, >=9301, 3721-5580, NA, 5581-9300,…
$ ingresos_fam          <fct> NA, 5581-9300, >=9301, 3721-5580, NA, 5581-9300,…
$ prof_cat              <fct> Administrative and Others, Nurses, Pyshicians, P…
$ hosp                  <fct> Hospital Nacional G. Almenara Irigoyen, Hospital…
$ area_cat              <fct> "Administ., apoyo al diagnostico y otros", "Admi…
$ modal_cat             <fct> Presential/Mix, Presential/Mix, Presential/Mix, …
$ height                <dbl> 165, 157, 159, 172, 163, 177, 170, 183, 168, 165…
$ weight                <int> 67, 82, 62, 76, 100, 78, 72, 96, 68, 72, 72, 56,…
$ bmi                   <dbl> 24.61, 33.27, 24.52, 25.69, 37.64, 24.90, 24.91,…
$ bmicat                <fct> Normal interval, Obesity, Normal interval, Overw…
$ ncomorb               <dbl> 0, 0, 1, 3, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ ncomorb2              <fct> No comorbidities, No comorbidities, At least 1 c…
$ com_hta               <fct> No, No, Yes, Yes, No, Yes, No, No, No, No, Yes, …
$ com_diab              <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_obes              <fct> No, No, No, No, Yes, No, No, No, No, No, No, No,…
$ com_epc               <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_trasp             <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_cancer            <fct> No, No, No, Yes, No, No, No, No, No, No, No, No,…
$ com_asma              <fct> No, No, No, Yes, No, No, No, No, No, No, No, No,…
$ com_desnut            <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_hipot             <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_vih               <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ com_otro              <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ inmunosup             <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes…
$ infprev               <fct> No, No, Yes, No, No, No, No, No, No, No, No, No,…
$ inf2                  <fct> No, No, Yes, No, No, No, No, No, No, No, No, No,…
$ boost                 <fct> Pfizer, Pfizer, Pfizer, Pfizer, Pfizer, Pfizer, …
$ boost_date            <date> 2021-10-16, 2021-11-11, 2021-10-15, 2021-10-15,…
$ boost_react           <fct> Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes,…
$ boost_react_dolor     <fct> Yes, No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, …
$ boost_react_enroj     <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_inflam    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_malest    <fct> No, Yes, No, No, No, No, No, No, Yes, No, No, Ye…
$ boost_react_cefalea   <fct> No, Yes, No, No, Yes, No, No, No, No, No, Yes, Y…
$ boost_react_fatiga    <fct> No, No, No, No, No, No, No, No, No, No, No, Yes,…
$ boost_react_somno     <fct> No, No, No, No, No, No, No, No, No, No, No, Yes,…
$ boost_react_fiebre    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_escal     <fct> No, No, No, No, No, No, No, No, Yes, No, No, No,…
$ boost_react_nausea    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_vomito    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_diarrea   <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_dolmusc   <fct> No, No, Yes, No, No, No, No, No, No, No, No, No,…
$ boost_react_dolart    <fct> No, Yes, No, No, No, No, No, No, No, No, No, No,…
$ boost_react_alerg     <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_hipotens  <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_hipertens <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_taquicard <fct> No, Yes, No, No, No, No, No, No, No, No, No, No,…
$ boost_react_mareo     <fct> No, No, No, Yes, No, No, No, No, No, No, No, No,…
$ boost_react_dolpecho  <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_cansancio <fct> No, Yes, No, No, No, No, No, No, No, No, No, No,…
$ boost_react_rinorrea  <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_adenop    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_granuloma <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_disfon    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_tos       <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_dolgarg   <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_uter      <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_insomnio  <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_urticaria <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_inapet    <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ boost_react_tend      <fct> No, No, No, No, No, No, No, No, No, No, No, No, …
$ date_dosis1           <date> 2021-02-19, 2021-03-10, 2021-02-11, 2021-03-01,…
$ date_dosis2           <date> 2021-03-12, 2021-03-31, 2021-03-04, 2021-03-22,…
$ date_igg              <date> 2021-12-02, 2021-12-09, 2021-11-24, 2021-11-23,…
$ tfdos1boost           <dbl> 239, 246, 246, 228, 237, 231, 248, 246, 244, 240…
$ tfdos2boost           <dbl> 218, 225, 225, 207, 216, 210, 227, 225, 223, 219…
$ tfboostig             <dbl> 47, 28, 40, 39, 37, 54, 36, 44, 42, 37, 36, 38, …
$ igg                   <dbl> 6290, 8970, 12700, 4600, 17800, 7310, 7130, 6910…
$ logigg                <dbl> 8.746716, 9.101641, 9.449357, 8.433812, 9.786954…
$ igg_pos               <fct> Positive, Positive, Positive, Positive, Positive…

skim(data)

Data summary

Name	data
Number of rows	595
Number of columns	76
_______________________
Column type frequency:
character	1
Date	4
factor	61
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
record_id	0	1	8	8	0	595	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
boost_date	0	1	2021-08-03	2021-12-18	2021-10-17	38
date_dosis1	0	1	2021-02-09	2021-04-19	2021-02-13	37
date_dosis2	0	1	2021-03-02	2021-07-03	2021-03-08	42
date_igg	0	1	2021-11-15	2022-01-18	2021-11-26	33

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
agecat	0	1.00	FALSE	3	20-: 269, 45-: 235, 60+: 91
agecat2	0	1.00	FALSE	5	(30: 173, (40: 167, (50: 132, (60: 81
sex	0	1.00	FALSE	2	Fem: 425, Mal: 170
nac	0	1.00	FALSE	2	Per: 590, For: 5
ingresos_per	192	0.68	FALSE	7	372: 116, 558: 112, 279: 61, 186: 42
ingresos_fam	228	0.62	FALSE	7	558: 100, >=9: 99, 372: 80, 279: 34
prof_cat	0	1.00	FALSE	4	Pys: 187, Nur: 168, Tec: 146, Adm: 94
hosp	3	0.99	FALSE	7	Hos: 257, Hos: 186, Hos: 118, Vil: 24
area_cat	1	1.00	FALSE	5	Hos: 162, Adm: 141, Eme: 118, UCI: 104
modal_cat	0	1.00	FALSE	2	Pre: 539, Lic: 56
bmicat	0	1.00	FALSE	3	Ove: 283, Nor: 215, Obe: 97
ncomorb2	0	1.00	FALSE	2	No : 411, At : 184
com_hta	0	1.00	FALSE	2	No: 531, Yes: 64
com_diab	0	1.00	FALSE	2	No: 568, Yes: 27
com_obes	0	1.00	FALSE	2	No: 554, Yes: 41
com_epc	0	1.00	FALSE	1	No: 595, Yes: 0
com_trasp	0	1.00	FALSE	1	No: 595, Yes: 0
com_cancer	0	1.00	FALSE	2	No: 583, Yes: 12
com_asma	0	1.00	FALSE	2	No: 550, Yes: 45
com_desnut	0	1.00	FALSE	1	No: 595, Yes: 0
com_hipot	0	1.00	FALSE	2	No: 554, Yes: 41
com_vih	0	1.00	FALSE	2	No: 594, Yes: 1
com_otro	0	1.00	FALSE	1	No: 595, Yes: 0
inmunosup	0	1.00	FALSE	2	Yes: 518, No: 77
infprev	0	1.00	FALSE	2	No: 358, Yes: 237
inf2	0	1.00	FALSE	2	No: 358, Yes: 237
boost	0	1.00	FALSE	1	Pfi: 595, Ast: 0, Sin: 0
boost_react	0	1.00	FALSE	2	Yes: 483, No: 112
boost_react_dolor	0	1.00	FALSE	2	Yes: 420, No: 175
boost_react_enroj	0	1.00	FALSE	2	No: 585, Yes: 10
boost_react_inflam	0	1.00	FALSE	2	No: 575, Yes: 20
boost_react_malest	0	1.00	FALSE	2	No: 446, Yes: 149
boost_react_cefalea	0	1.00	FALSE	2	No: 486, Yes: 109
boost_react_fatiga	0	1.00	FALSE	2	No: 561, Yes: 34
boost_react_somno	0	1.00	FALSE	2	No: 539, Yes: 56
boost_react_fiebre	0	1.00	FALSE	2	No: 526, Yes: 69
boost_react_escal	0	1.00	FALSE	2	No: 565, Yes: 30
boost_react_nausea	0	1.00	FALSE	2	No: 578, Yes: 17
boost_react_vomito	0	1.00	FALSE	2	No: 588, Yes: 7
boost_react_diarrea	0	1.00	FALSE	2	No: 587, Yes: 8
boost_react_dolmusc	0	1.00	FALSE	2	No: 551, Yes: 44
boost_react_dolart	0	1.00	FALSE	2	No: 569, Yes: 26
boost_react_alerg	0	1.00	FALSE	2	No: 592, Yes: 3
boost_react_hipotens	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_hipertens	0	1.00	FALSE	1	No: 595, Yes: 0
boost_react_taquicard	0	1.00	FALSE	2	No: 593, Yes: 2
boost_react_mareo	0	1.00	FALSE	2	No: 590, Yes: 5
boost_react_dolpecho	0	1.00	FALSE	2	No: 592, Yes: 3
boost_react_cansancio	0	1.00	FALSE	2	No: 593, Yes: 2
boost_react_rinorrea	0	1.00	FALSE	2	No: 593, Yes: 2
boost_react_adenop	0	1.00	FALSE	1	No: 595, Yes: 0
boost_react_granuloma	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_disfon	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_tos	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_dolgarg	0	1.00	FALSE	2	No: 593, Yes: 2
boost_react_uter	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_insomnio	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_urticaria	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_inapet	0	1.00	FALSE	2	No: 594, Yes: 1
boost_react_tend	0	1.00	FALSE	2	No: 594, Yes: 1
igg_pos	0	1.00	FALSE	1	Pos: 595, Neg: 0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	0	1	46.10	11.11	20.00	37.00	46.00	54.00	70.00	▂▇▇▆▃
height	0	1	160.66	8.41	140.00	155.00	160.00	166.00	186.00	▁▇▆▃▁
weight	0	1	69.06	13.42	45.00	60.00	67.00	78.00	158.00	▇▇▁▁▁
bmi	0	1	26.62	3.84	18.82	24.33	25.97	28.44	58.03	▇▅▁▁▁
ncomorb	0	1	0.39	0.66	0.00	0.00	0.00	1.00	5.00	▇▁▁▁▁
tfdos1boost	0	1	244.37	10.08	159.00	240.00	246.00	248.00	306.00	▁▁▇▅▁
tfdos2boost	0	1	222.80	11.17	113.00	219.00	224.00	227.00	285.00	▁▁▁▇▁
tfboostig	0	1	41.51	13.00	20.00	32.00	38.00	48.00	157.00	▇▂▁▁▁
igg	0	1	10446.99	7699.77	116.00	5115.00	8410.00	13000.00	41600.00	▇▅▂▁▁
logigg	0	1	8.99	0.79	4.75	8.54	9.04	9.47	10.64	▁▁▂▇▃

2.4 Replicar Tabla 1

Ejercicio 1

Seleccione las variables para crear la tabla 1. Las variables son las siguientes: age, agecat2, sex, nac, ingresos_per, ingresos_fam, prof_cat, hosp, area_cat, modal_cat, height, weight, ncomorb, bmi, bmicat, com_hta, com_diab, com_epc:com_vih, inmunosup, infprev, boost, tfdos1boost, tfdos2boost, tfboostig, igg, igg_pos, boost_react (mantenga el orden). Usando gtsummary, cree la tabla 1 que mejor replique la presentada en el artículo.

data |>  
  select(age, agecat2, sex, nac, ingresos_per, ingresos_fam, 
         prof_cat, hosp, area_cat, modal_cat, height, weight, 
         ncomorb, bmi,  bmicat, com_hta, com_diab, com_epc:com_vih,
         inmunosup, infprev, boost, tfdos1boost, tfdos2boost, 
         tfboostig, igg, igg_pos, boost_react) |> 
  tbl_summary(statistic = list(all_continuous() ~ "{median} [{p25}, {p75}]", 
                               all_categorical() ~ "{n} ({p})")) |> 
  bold_labels()

Characteristic	N = 5951
Age (years)	46 [37, 54]
Age group
[20,30]	42 (7.1)
(30,40]	173 (29)
(40,50]	167 (28)
(50,60]	132 (22)
(60,70]	81 (14)
Sex
Male	170 (29)
Female	425 (71)
Nationality
Peru	590 (99)
Foreign	5 (0.8)
Personal income per month
<930	7 (1.7)
931-1860	24 (6.0)
1861-2790	42 (10)
2791-3720	61 (15)
3721-5580	116 (29)
5581-9300	112 (28)
>=9301	41 (10)
Unknown	192
Familiar income per month
<930	8 (2.2)
931-1860	13 (3.5)
1861-2790	33 (9.0)
2791-3720	34 (9.3)
3721-5580	80 (22)
5581-9300	100 (27)
>=9301	99 (27)
Unknown	228
Profession
Administrative and Others	94 (16)
Nurses	168 (28)
Pyshicians	187 (31)
Technical nurses	146 (25)
Hospital
Hospital Nacional E. Rebagliati Martins	257 (43)
Hospital Nacional A. Sabogal Sologuren	186 (31)
Hospital Nacional G. Almenara Irigoyen	118 (20)
Villa Mongrut	24 (4.1)
H. Angamos	4 (0.7)
Villa Panamericana	2 (0.3)
Policlinico PIASA	1 (0.2)
Unknown	3
Work area
Administ., apoyo al diagnostico y otros	141 (24)
Consulta externa/domic	69 (12)
Emergencia	118 (20)
Hospitalización	162 (27)
UCI	104 (18)
Unknown	1
Work modality
License/Teleworking	56 (9.4)
Presential/Mix	539 (91)
Height (cm)	160 [155, 166]
Weight (kg)	67 [60, 78]
Number of comorbidities
0	411 (69)
1	146 (25)
2	31 (5.2)
3	6 (1.0)
5	1 (0.2)
Body mass index (kg/m2)	26.0 [24.3, 28.4]
BMI group
Normal interval	215 (36)
Overweight	283 (48)
Obesity	97 (16)
Hypertension	64 (11)
Diabetes	27 (4.5)
Chronic lung disease	0 (0)
Organ transplant	0 (0)
Cancer	12 (2.0)
Asthma	45 (7.6)
Undernourishment	0 (0)
Hypothyroidism	41 (6.9)
HIV	1 (0.2)
Immunosuppressive condition	518 (87)
Previous infection	237 (40)
Booster scheme
Pfizer	595 (100)
Astrazeneca	0 (0)
Sinopharm	0 (0)
Time from dosis 1 to boosting	246 [240, 248]
Time from dosis 2 to boosting	224 [219, 227]
Time from boosting to IgG measure	38 [32, 48]
Antibodies Anti-SARS-CoV-2 levels (BAU/mL)	8,410 [5,115, 13,000]
Seropositivity
Negative	0 (0)
Positive	595 (100)
Reactogenicity	483 (81)
1 Median [IQR]; n (%)

Ejercicio 2

Cree una tabla nueva (no presentada en el artículo) según la variable respuesta rectogenicidad (boost_react).

data |>  
  select(age, agecat2, sex, nac, ingresos_per, ingresos_fam, 
         prof_cat, hosp, area_cat, modal_cat, height, weight, 
         ncomorb, bmi,  bmicat, com_hta, com_diab, com_epc:com_vih,
         inmunosup, infprev, boost, tfdos1boost, tfdos2boost, 
         tfboostig, igg, igg_pos, boost_react) |> 
  tbl_summary(statistic = list(all_continuous() ~ "{median} [{p25}, {p75}]", 
                               all_categorical() ~ "{n} ({p})"), 
              by =  boost_react, 
              percent = "row") |> 
  bold_labels()

Characteristic	Yes, N = 4831	No, N = 1121
Age (years)	46 [37, 54]	46 [36, 56]
Age group
[20,30]	29 (69)	13 (31)
(30,40]	142 (82)	31 (18)
(40,50]	141 (84)	26 (16)
(50,60]	109 (83)	23 (17)
(60,70]	62 (77)	19 (23)
Sex
Male	139 (82)	31 (18)
Female	344 (81)	81 (19)
Nationality
Peru	479 (81)	111 (19)
Foreign	4 (80)	1 (20)
Personal income per month
<930	7 (100)	0 (0)
931-1860	16 (67)	8 (33)
1861-2790	25 (60)	17 (40)
2791-3720	48 (79)	13 (21)
3721-5580	100 (86)	16 (14)
5581-9300	91 (81)	21 (19)
>=9301	32 (78)	9 (22)
Unknown	164	28
Familiar income per month
<930	8 (100)	0 (0)
931-1860	7 (54)	6 (46)
1861-2790	21 (64)	12 (36)
2791-3720	27 (79)	7 (21)
3721-5580	67 (84)	13 (16)
5581-9300	88 (88)	12 (12)
>=9301	77 (78)	22 (22)
Unknown	188	40
Profession
Administrative and Others	74 (79)	20 (21)
Nurses	151 (90)	17 (10)
Pyshicians	159 (85)	28 (15)
Technical nurses	99 (68)	47 (32)
Hospital
Hospital Nacional E. Rebagliati Martins	217 (84)	40 (16)
Hospital Nacional A. Sabogal Sologuren	147 (79)	39 (21)
Hospital Nacional G. Almenara Irigoyen	92 (78)	26 (22)
Villa Mongrut	21 (88)	3 (13)
H. Angamos	2 (50)	2 (50)
Villa Panamericana	1 (50)	1 (50)
Policlinico PIASA	1 (100)	0 (0)
Unknown	2	1
Work area
Administ., apoyo al diagnostico y otros	116 (82)	25 (18)
Consulta externa/domic	51 (74)	18 (26)
Emergencia	103 (87)	15 (13)
Hospitalización	130 (80)	32 (20)
UCI	82 (79)	22 (21)
Unknown	1	0
Work modality
License/Teleworking	44 (79)	12 (21)
Presential/Mix	439 (81)	100 (19)
Height (cm)	160 [155, 166]	160 [155, 165]
Weight (kg)	67 [59, 78]	68 [61, 78]
Number of comorbidities
0	330 (80)	81 (20)
1	121 (83)	25 (17)
2	27 (87)	4 (13)
3	5 (83)	1 (17)
5	0 (0)	1 (100)
Body mass index (kg/m2)	26.1 [24.2, 28.5]	25.8 [24.7, 28.4]
BMI group
Normal interval	180 (84)	35 (16)
Overweight	223 (79)	60 (21)
Obesity	80 (82)	17 (18)
Hypertension	51 (80)	13 (20)
Diabetes	23 (85)	4 (15)
Chronic lung disease	0 (NA)	0 (NA)
Organ transplant	0 (NA)	0 (NA)
Cancer	9 (75)	3 (25)
Asthma	36 (80)	9 (20)
Undernourishment	0 (NA)	0 (NA)
Hypothyroidism	34 (83)	7 (17)
HIV	1 (100)	0 (0)
Immunosuppressive condition	421 (81)	97 (19)
Previous infection	194 (82)	43 (18)
Booster scheme
Pfizer	483 (81)	112 (19)
Astrazeneca	0 (NA)	0 (NA)
Sinopharm	0 (NA)	0 (NA)
Time from dosis 1 to boosting	246 [240, 248]	246 [240, 249]
Time from dosis 2 to boosting	224 [219, 227]	225 [219, 227]
Time from boosting to IgG measure	38 [32, 48]	40 [33, 50]
Antibodies Anti-SARS-CoV-2 levels (BAU/mL)	8,530 [5,270, 13,450]	7,820 [4,743, 11,525]
Seropositivity
Negative	0 (NA)	0 (NA)
Positive	483 (81)	112 (19)
1 Median [IQR]; n (%)

Ejercicio 3

Observe la Tabla 2 y haga una lectura crítica de esta. ¿Qué aspecto del análisis y presentación de resultados no aporta valor y es considerado una práctica que debe evitarse?

# Escriba el código aquí

2.5 Replicar Figura 1

Ejercicio 4

Trate de replicar cada uno de los gráficos de la Figura 1. Cree la Fig1A, Fig1B, etc por separado.

• Grafico de densidad

data |> 
  ggplot(aes(x = igg)) + 
  geom_density(fill = "red", alpha = 0.25) +
  labs(x = "Density", y = "IgG (BAU/ml)") + 
  theme_bw() -> g0

g0

library(viridis)
library(ggsci)
library(scales)
pd <- position_dodge(0.3)
alfa <- 0.1
# Density plot----
data %>% 
  ggplot() + 
  aes(x = igg) + 
  geom_density(alpha = 0.25, fill = "red") + 
  labs(y = "Density", x = "IgG (BAU/ml)") + 
  guides(fill = guide_legend(title = "")) + 
  theme_bw() + 
  theme(legend.position = c(0.90, 0.90)) + 
  scale_fill_lancet() -> g0; g0

# Work/modality----
pd <- position_dodge(0.3)
alfa <- 0.1
data %>% 
  mutate(modal_cat = factor(modal_cat, 
                            levels = c("License/Teleworking", 
                                       "Presential/Mix"), 
                            labels = c("Work leave/remote", 
                                       "Full/part-time in-person"))) %>% 
  arrange(igg) %>% 
  ggplot() +
  aes(x = modal_cat, y = igg, group = modal_cat, 
      color = modal_cat, alpha = 0.1) + 
  geom_boxplot(aes(group = modal_cat), outlier.shape = NA) + 
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=1, alpha=0.9) +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  geom_point(position = pd, size = 0.1) + 
  labs(y = "IgG after booster (BAU/mL)", 
       x = "Working modality") +
  scale_y_continuous(trans = "log", labels = comma) + 
  theme_bw() + 
  theme(legend.position = "none") + 
  scale_color_lancet() -> g1; g1

# Previous infection----
# infprev
pd <- position_dodge(0.3)
alfa <- 0.1
data %>% 
  arrange(igg) %>% 
  ggplot() +
  aes(x = infprev, y = igg, group = infprev, 
      color = infprev, alpha = 0.1) + 
  geom_boxplot(aes(group = infprev), outlier.shape = NA) + 
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=1, alpha=0.9) +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  geom_point(position = pd, size = 0.1) + 
  labs(y = "IgG after booster (BAU/mL)", 
       x = "Previous infection") +
  scale_y_continuous(trans = "log", labels = comma) + 
  theme_bw() + 
  theme(legend.position = "none") + 
  scale_color_lancet() -> g2; g2

# Inmunosuprresive condition----
# inmunosup
pd <- position_dodge(0.3)
alfa <- 0.1
data %>% 
  arrange(igg) %>% 
  ggplot() +
  aes(x = inmunosup, y = igg, group = inmunosup, 
      color = inmunosup, alpha = 0.1) + 
  geom_boxplot(aes(group = inmunosup), outlier.shape = NA) + 
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=1, alpha=0.9) +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  geom_point(position = pd, size = 0.1) + 
  labs(y = "IgG after booster (BAU/mL)", 
       x = "Immunosuppressive condition") +
  scale_y_continuous(trans = "log", labels = comma) + 
  theme_bw() + 
  theme(legend.position = "none") + 
  scale_color_lancet() -> g3; g3

# Diabetes----
# com_diab
pd <- position_dodge(0.3)
alfa <- 0.1
data %>% 
  arrange(igg) %>% 
  ggplot() +
  aes(x = com_diab, y = igg, group = com_diab, 
      color = com_diab, alpha = 0.1) + 
  geom_boxplot(aes(group = com_diab), outlier.shape = NA) + 
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=1, alpha=0.9) +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  geom_point(position = pd, size = 0.1) + 
  labs(y = "IgG after booster (BAU/mL)", 
       x = "Diabetes") +
  scale_y_continuous(trans = "log", labels = comma) + 
  theme_bw() + 
  theme(legend.position = "none") + 
  scale_color_lancet() -> g4; g4

# Profession----
# prof_cat
pd <- position_dodge(0.3)
alfa <- 0.1
data %>% 
  mutate(prof_cat = factor(prof_cat, 
                           levels = c("Administrative and Others", 
                                      "Nurses", 
                                      "Pyshicians", 
                                      "Technical nurses"), 
                           labels = c("Admin/Others", 
                                      "Nurses", 
                                      "Physicians", 
                                      "Technical nurses"))) %>%
  arrange(igg) %>% 
  ggplot() +
  aes(x = prof_cat, y = igg, group = prof_cat, 
      color = prof_cat, alpha = 0.1) + 
  geom_boxplot(aes(group = prof_cat), outlier.shape = NA) + 
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=1, alpha=0.9) +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  geom_point(position = pd, size = 0.1) + 
  labs(y = "IgG after booster (BAU/mL)", 
       x = "Profession") +
  scale_y_continuous(trans = "log", labels = comma) + 
  theme_bw() + 
  theme(legend.position = "none") + 
  scale_color_lancet() -> g5; g5

library(patchwork)

g0 / g1

g0 | g1

(g0 / g1) | (g2 / plot_spacer()) | ( plot_spacer() / g3)

(g0 | g1) + 
  plot_annotation(tag_levels = "A")

plot_biv_cat <- (g0 | g1 | g2) / (g3 | g4 | g5) + 
  plot_annotation(tag_levels = "A")

plot_biv_cat

ggsave(filename = "Grafico_Paper.jpg", 
       device =  "jpg", 
       plot = plot_biv_cat, 
       scale = 2, 
       width = 3903, 
       height = 1822, 
       units = "px", 
       dpi = 600
       )

Ejercicio 5

Trate de replicar cada uno de los gráficos de la Figura 1. Cree la Fig1A, Fig1B, etc por separado.

# Escriba el código aquí

Ejercicio 6

Fusione los gráficos usando el paquete {patchwork} para replicar a la Figura 1.

# Escriba el código aquí

Ejercicio 7

Guarde el gráfico en formato “png”.

# Escriba el código aquí

Ejercicio 8

Imprima el gráfico en foramto “png” dentro del archivo quarto.

# Escriba el código aquí

include_graphics("Grafico_Paper.png")

Ejercicio 9

Renderice todo el quarto. Cambie el nombre del archivo html generado.

# Escriba el código aquí

Ejercicio 10

Modifique el YAML del Quarto para que la presentación de este mejore. Renderice el quarto final y compare con la primera versión del html generado en el ejercicio 9.

# Escriba el código aquí